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Record W7005681321

Screening for HIV-Associated Neurocognitive Impairment

2019· article· en· W7005681321 on OpenAlexaboutno aff

Bibliographic record

VenueMurdoch Research Repository (Murdoch University) · 2019
Typearticle
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsNeurocognitiveMontreal Cognitive AssessmentMoodAffect (linguistics)DementiaContext (archaeology)CognitionQuality of life (healthcare)
DOInot available

Abstract

fetched live from OpenAlex

Neurocognitive impairment (NCI) is common in people aging with HIV and can adversely affect health-related quality of life. However, early NCI may be largely asymptomatic and neurocognitive function is rarely assessed in the context of routine clinical care. In this study, we considered the utility of two assessment tools as screens for NCI in patients attending a community-based clinic (N=58; mean age=57 years): the Montreal Cognitive Assessment (MoCA) and a 3-item cognitive concerns questionnaire derived from the HIV Dementia Scale. Health-related quality of life and depression/anxiety were also measured. Indication of NCI using the MoCA was more prevalent compared to the 3-item questionnaire and was associated with the patients’ initial antiretroviral therapy commencing between the years of 1997 and 2001, independently of age. Findings of the MoCA were not confounded by existing mood disorders, unlike the 3-item questionnaire. Therefore, we suggest implementing the MoCA as an initial screen for NCI.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.037
GPT teacher head0.288
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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